A Neural Network Approach to Nonlinear Model Predictive Control

被引：0

作者：

Yan, Zheng ^{[1
]}

Wang, Jun ^{[1
]}

机构：

[1] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Shatin, Hong Kong, Peoples R China

来源：

IECON 2011: 37TH ANNUAL CONFERENCE ON IEEE INDUSTRIAL ELECTRONICS SOCIETY | 2011年

关键词：

QUADRATIC-PROGRAMMING PROBLEMS; OPTIMIZATION; REDUNDANCY; SUBJECT;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper proposes a neural network approach to nonlinear model predictive control (NMPC). The NMPC problem is formulated as a convex programming problem via Jacobain linearization. The unknown high-order term associated with the linearization is estimated by using a feedforward neural network via supervised learning. The convex optimization problem involved in MPC is solved by using a recurrent neural network. Simulation results are provided to demonstrate the performance of the approach.

引用

页码：2305 / 2310

页数：6

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